Machine-to-machine applications are gaining more and more importance nowadays leading to standards like ETSI M2M, one M2M or the like. Conventional machine-to-machine systems such as specified by ETSI M2M provide well defined machine-to-machine resources to represent things of the real world in electronic form. To provide consistent data translation and data interoperability to heterogeneous machine-to-machine applications semantic annotation of machine-to-machine resources can be used for adding semantic information to machine-to-machine. Such semantically annotated machine-to-machine resources can be contacted by a machine-to-machine application. The machine-to-machine application understands what data is provided by the machine-to-machine resources and the meaning of this data.
Since machine-to-machine resources conventionally comprise sensor devices monitoring and reporting a specific data and actuators executing a given command a semantic machine-to-machine system needs to provide semantic information for both data and commands. Other semantic systems which enable semantic communication store semantic information in a so-called triplestore based database. However, conventional machine-to-machine systems do not use triplestore based databases and as a consequence cannot use for example SPARQL semantic queries and provide semantic information.
To annotate semantic information in a conventional machine-to-machine system the semantic information can be embedded into the ETSI M2M resource format. Another option is to convert the entire ETSI M2M system to a new system using triplestore. However, although all semantic technologies like RDF, OWL, SPARQL, etc. can then be used, this is not feasible because it builds a totally new system and causes other problems, for example, compatibility with legacy ETSI machine-to-machine systems is not ensured.
Semantic information can be annotated into the resource format of an already existing ETSI machine-to-machine system with minimal updates. However only SPARQL commands can be used for the semantic search. In other cases for example semantic information is annotated using another format like the Resource Description Framework RDF, since the Resource Description Framework enables in a general, flexible way to decompose any knowledge into discrete pieces and RDF can be stored in different formats. Since RDF is useful to encode information about relation between things including a lot of semantic information, usually a triplestore-based database is selected in order to store and retrieve such relational information. However, this has the significant drawback, that semantic queries such as SPARQL cannot be used because the machine-to-machine system does not have any semantic information and is not able to understand semantic meaning contained in a semantic query.